We benchmarked 4 AI models on refactoring real-world DAX — the results surprised us

Summary

A recent benchmark of four AI models for DAX refactoring reveals significant performance differences.

What happened?

The DaxAudit.com tool analyzed 20 complex DAX expressions from their production environment and tested four AI models: DeepSeek V3.2, Qwen 3.5 397B, GLM-5, and a fourth model. Each of these models refactored the DAX expressions, with character counts ranging from 700 to 4,200, utilizing real scenarios rather than hypothetical examples.

Why this matters

For BI professionals, selecting the right AI tool is crucial for optimizing DAX performance in Power BI. The outcomes of this benchmark shed light on the effectiveness of AI models in DAX refactoring. It demonstrates that not all models are created equal, and these disparities can impact the speed and efficiency of reporting processes. Competitors like Tableau and Looker are also exploring AI-driven solutions, highlighting the need to evaluate existing tools.

Concrete takeaway

BI professionals should consider the performance of different AI models for DAX refactoring when making software choices. It is essential to assess benchmarks and user experiences to maximize the impact on overall BI strategy and report quality.